Although dozens of protein biomarkers have been associated individually or in panels with cardiovascular risk, their incremental utility has been modest. For new biomarkers to add incremental utility, they must provide non-overlapping information. However, new biomarkers typically emerge from well-studied pathways that are already covered by existing biomarkers. Thus, it is necessary to consider alternative approaches to identifying protein biomarkers of cardiovascular disease (CVD) ? to provide novel biologic information and to ultimately enable more efficient targeting of preventive interventions. Emerging proteomic technologies are beginning to permit the systematic, unbiased characterization of human plasma samples, though few data exist in population-based cohort studies. To address limitations of prior studies, we have established a high-throughput proteomic platform in our laboratory that measures 1129 proteins using chemically modified single-stranded DNA aptamers. We have rigorously addressed intra- and inter-assay variability, acquiring data >10-fold faster than mass spectrometry based methods. In pilot analyses in the Framingham Heart Study (FHS), we confirmed established correlations of known biomarkers with the Framingham Risk Score and discovered many new associations. We therefore propose to integrate novel aptamer based proteomic analyses with the rich phenotypic data in FHS, as well as genetic scans for common and rare variants. We will test the hypothesis that proteomic profiling in well-phenotyped populations will illuminate additional disease pathways apart from those already described. In Specific Aim 1, we will identify novel proteomic biomarkers of cardiovascular risk factors (BMI, systolic blood pressure, total/HDL cholesterol, diabetes, smoking and the Framingham Risk Score) in cross-sectional analyses. In Specific Aim 2, we will assess whether novel proteomic biomarkers predict the risk of future CVD events in prospective analyses. In Specific Aim 3, we will characterize the genetic determinants of proteins associated with cardiovascular risk. We will analyze the genetic determinants of proteins identified in Aims 1 and 2, and examine whether genetic variants that determine protein levels are in turn associated with clinical traits. All of the primary data generated by this multi-omics proposal wil be made broadly available in real time to the scientific community.